1. Field of the Invention
Exemplary embodiments of the present invention relate to digital video broadcasting, and more particularly, to a decision-feedback equalizer, which may receive a variety of digital information and a method of updating coefficients thereof.
2. Description of the Conventional Art
An equalizer for digital video broadcasting may be needed for large amounts of digital information, for example, voice, data, and/or video communications. Such digital information may be transmitted via various transmission mediums, which may have different transmission characteristics. Transmission mediums may cause different kinds of ghosting, for example, frequency-dependent phase, amplitude distortion, multipath receiving, or voice echoes, and various types of fading in signals, for example, Rayleigh fading. Data transmissions may suffer from a noise, for example, additive white Gaussian noise. The equalizer may be used to reduce echoes and/or video ghosts and control signals for wireless modems and/or telephones.
In digital communications, data transmission over intersymbol interference (ISI) channels may be a problem. The ISI may occur when pulsed information, for example, amplitude-modulated digital transmissions, may be transmitted over analog channels, for example, telephone lines and/or skywave channels.
Maximum-likelihood sequence estimation (MLSE) may achieve an improved symbol error rate (SER), but may become more complex with the length of the channel time dispersion. Extremely high complexity of the MLSE in a software and/or hardware may limit its use.
A linear equalizer (LE) may detect and equalize ISI. LE may have a complexity, which may be a linear function of the channel dispersion length and may suffer from significant noise enhancement. The performance of the linear equalizer may be worse than the performance of an MLSE.
A decision-feedback equalizer (DFE) may have a lower complexity and/or improved performance.
FIG. 1 is an example of a construction diagram of a conventional DFE. The DFE may use previously decoded data symbols in order to calculate and reduce intersymbol interference (ISI). The performance of the DFE may be degraded due to incorrect decisions in a decision feedback filter, for example, when a channel introduces strong ghosts, for example, during a single frequency network operation in digital television broadcasting.
Referring to FIG. 1, a DFE may include a feedforward filter 102, a feedback filter 103, a slicer 104, and an adder 105. A received digital signal 101 may be input to the feedforward filter 102.
The feedforward filter 102 may partially correct signal errors using a filter having a magnitude opposite to a magnitude of the input digital signal 101. The slicer 104 may be, for example, a decision device which may be based on the magnitudes of received signals and may classify received signals based on decisions of 0, ±2, ±4, and ±6 in order. The received signals may be classified into symbols corresponding to normalized signals of ±1, ±3, ±5, and ±7. The slider 104 may be a multi-dimensional slicer, which may be used in, for example, quadrature amplitude modulation (QAM) systems.
The adder 105 may add the output of the feedforward filter 102 and the output of the feedback filter 103 and may output the result to the slicer 104. The feedforward filter 102 may reduce noises using a filter having a magnitude opposite to a magnitude of the input digital signal 101.
A decision-feedback sequence estimation (DFSE) algorithm may provide a tradeoff between performance and complexity.
Wireless communication systems employ trellis-coded modulation (TCM).
FIG. 2 illustrates a conventional TCM scheme for 8-level amplitude modulated signals.
Referring to FIG. 2, a TCM encoder may be comprised of an 8-VSB trellis encoder 201 and an 8-level symbol mapper 203. The 8-VSB trellis encoder 201 may employ an 8-level 3-bit 1-dimensional arrangement. The 8-VSB trellis encoder may use a ⅔ rate trellis code.
A method for detection of trellis-coded symbols in channels without ISI may be the MLSE. The number of trellis states in codes used for TCM may be smaller and the complexity of the MLSE may not be higher. The MLSE may be implemented using a Viterbi algorithm (or Viterbi decoding algorithm). The TCM symbols transmitted through ISI-free channels may be detected with improved performance.
When channels introduce ISI, the MLSE detector which takes into account the ISI introduced by the channels and the TCM may become more complex. A DFE may be used for the compensation of channel ISI and a MLSE (Viterbi) decoder to decode the TCM.
FIG. 3 is a construction diagram of a conventional DFE combined with a TCM decoder.
Referring to FIG. 3, the DFE combined with the TCM decoder may comprise a feedforward filter 302, a feedback filter 303, a slicer 304, and an adder 305, and a TCM decoder 307 which may decode trellis-coded symbols. A received digital signal may be input to the DFE via an input line 301 and output via an output line 306 connected to the TCM decoder 307.
The DFE may be operated before the TCM decoder uses uncoded symbols to perform a feedback operation and the reliability of the uncoded symbols may be lower. The performance may be worse than that of a joint (channel+TCM) MLSE.
FIG. 4 is a construction diagram of another conventional DFE combined with a TCM decoder.
The DFSE algorithm may be used to decode TCM symbols transmitted through ISI channels.
Instead of using slicer decisions in a feedback filter, the DFE may use symbol decisions from the more likely surviving path of the Viterbi decoder. This scheme, sometimes referred to as “a Viterbi decoder with global decision feedback”, is illustrated in FIG. 4. Referring to FIG. 4, an adder 407 may add the output of a feedforward filter 402 and the output of a feedback filter 403 and may output the result to a TCM decoder 404. The TCM decoder 404 may decode symbols 405 and inputs the decoded symbols 405 to the feedback filter 403. The Viterbi decoder with global decision feedback may use the symbol decisions from the more likely surviving path of the TCM (Viterbi) decoder 404 as the inputs of the feedback filter 403. A decoding depth Nth symbol, which may be the more reliable symbol among the outputs of the TCM decoder 404, may become an output signal 406.
This combination of a DFE and a TCM (Viterbi) decoder, as shown in FIG. 4, may have improved performance over the scheme shown in FIG. 3, since the decisions from the TCM (Viterbi) decoder may be more reliable.
FIG. 5 is a construction diagram of a conventional DFE, which may use a least-mean square (LMS) algorithm for updating feedback filter coefficients.
In, for example, wireless applications of MLSE and DFE, the channel transfer function may be unknown at the receiver and/or time-variant. Any detection/equalization scheme used in wireless communication receivers may be adaptive, i.e., may be able to change coefficients of an equalizer and track channel variations. In the LMS scheme, equalizer coefficients may be recursively updated at every iteration of the algorithm. For example, feedback filter coefficients of a DFE may recursively be updated in accordance with an LMS algorithm as shown in Equation 1.bi(k+1)=bi(k)+μek{circumflex over (d)}k−i, i=1, 2, . . . , LB  (1)
Here, bi(k) are i-th feedback coefficients (518, 519, . . . , and 520) of a DFE at k-th iteration, LB is the number of feedback filter coefficients, {circumflex over (d)}k are decisions in a feedback filter, stored in delay lines (521, 522, . . . , and 523), μ is a step-size parameter (positive constant), and ek are error signals 508, which may be differences between the outputs 524 of the DFE and the decisions {circumflex over (d)}k 525. During a training period, the transmitted data sequence dk may be known and may be used by an equalizer to update the coefficients bi(k) in accordance with the LMS algorithm shown in Equation 1.
A DFE, which may use the LMS adaptation scheme embodied by Equation 1, is shown in FIG. 5. That is, FIG. 5 illustrates an example of applying the LMS adaptation scheme shown in Equation 1 to the DFE using the slicer of FIG. 1. After a training period, decisions {circumflex over (d)}k at the output of a slicer 510 may be more reliable and the decisions {circumflex over (d)}k may be used to update equalizer coefficients in accordance with the LMS algorithm.
FIG. 6 is a construction diagram of a conventional DFE, which may use both the LMS algorithm and a Stop-and-Go algorithm to update feedback filter coefficients.
The LMS algorithm may also be used without a training sequence.
The “Stop-and-Go” algorithm may disable adaptation if decisions are not reliable, and may update equalizer coefficients if the decisions are more likely to be correct. Detection of less reliable decisions and/or generation of enable/disable flags 623 may be performed in a ‘Stop-and-Go’ (SAG) block 618.
FIG. 7 is a construction diagram of a conventional DFE combined with a trellis decoder, which may use an LMS adaptation algorithm to update feedback filter coefficients.
The LMS algorithm shown in Equation 1 and its ‘Stop-and-Go’ variant may be used with the DDFSE scheme shown in FIG. 4.
The combined equalizer/decoder structure with the LMS adaptation algorithm shown in FIG. 7 may introduce an instability problem. An example of the instability of this scheme is illustrated in FIG. 8.
FIG. 8 is a graph showing the signal-to-noise ratio (SNR) versus the number of iterations for the DFE shown in FIG. 7.
FIG. 8 shows the simulation results for the DFE combined with the TCM Viterbi decoder shown in FIG. 7. A channel may have three equal, or substantially equal, amplitude paths and a transmission system may use 8-level amplitude modulated signals as shown in FIG. 2. The equalizer steady-state operation shown in FIG. 8 may not depend on resolution of the equalizer coefficients or overflow effects and may be a result of the TCM decision feedback properties.
For first periods of time, the DFE may be operated such that signal-to-noise ratio (SNR) may be more stable. After some periods of time, the stability of the SNR may be greatly lower and a variation thereof may be repeated periodically as the number of iterations increases.
In a convergence region, contribution of the decision-feedback part of the equalizer in ISI compensation may be insignificant, since decisions may be less reliable and the equalizer of FIG. 7 may not rely on the decision feedback mechanism. Decision errors may not affect equalizer stability in this region, and output signal-to-noise ratio (SNR) may be more stable.
After some period of time when decisions become more reliable, the equalizer may rely on these decisions and may use a feedback filter for ISI compensation.
FIG. 9A is a graph showing an example of the percentage of decision errors in the slicer and the TCM decoder as a function of time.
The TCM decision errors may be correlated and may group in error bursts rather than be distributed randomly as in the case of slicer decisions.
That is, if an error occurs at the output of the TCM decoder, the error may cause series (or bursts) of errors, and for some period of time the number of decision errors in a feedback filter may be higher.
FIG. 9A shows an example relationship between the percentage of errors in the decision feedback filter and a time. In case of using the TCM feedback scheme shown in FIG. 7, the number of decision errors in the feedback filter may be smaller, but sometimes the TCM decoder may introduce bursts of errors and the number of decision errors may increase.
In case of using the slicer shown in FIG. 5, the percentage of decision errors may be more stable (e.g., approximately 20%). A higher percentage of decision errors may decrease the overall equalizer performance, but may stabilize the adaptation scheme because the LMS algorithm may not rely as much on the feedback mechanism.
When the TCM feedback mechanism is used, the feedback filter may be free of errors, and the LMS algorithm may update equalizer coefficients in accordance with this error-free state of the feedback filter. The equalizer may rely on feedback ISI compensation and may become more sensitive to decision errors. The TCM decoder may introduce bursts of errors and the equalizer performance may be degraded as shown in FIG. 8.
In the ‘Stop-and-Go’ LMS algorithm, the adaptive scheme implemented may disable adaptation when decisions may be less reliable.